Diabetes Classification using Radial Basis Function Network by Combining Cluster Validity Index and BAT Optimization with Novel Fitness Function

Diabetes is one of the foremost causes for the increase in mortality among children and adults in recent years. Classification systems are being used by doctors to analyse and diagnose the medical data. Radial basis function neural networks are more attractive for classification of diseases, especially in diabetes classification, because of it’s non iterative nature. Radial basis function neural networks are four layer feed forward neural network with input layer, pattern layer, summation layer and the decision layer respectively. The size of the pattern layer increases on par with training data set size. Though various attempts have made to solve this issue by clustering input data using different clustering algorithms like k-means, k-medoids, and SOFM etc. However main difficulty of determining the optimal number of neurons in the pattern layer remain unsolved. In this paper, we present a new model based on cluster validity index with radial basis neural network for classification of diabetic patients data. We employ cluster validity index in class by class fashion for determining the optimal number of neurons in pattern layer. A new convex fitness function has also been designed for bat inspired optimization algorithm to identify the weights between summation layer and pattern layer. The proposed model for radial basis function neural network is tested on Pima Indians Diabetes data set and synthetic data sets. Experimental results proved that our approach performs better in terms of accuracy, sensitivity, specificity, classification time, training time, network complexity and computational time compared to conventional radial basis function neural network. It is also proved that proposed model performs better compared to familiar classifiers namely probabilistic neural network, feed forward neural network, cascade forward network, time delay network, artificial immuine system and GINI classifier.

Abstract licence: CC BY-NC-ND

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